自然资源时空数据管理技术研究进展

金子鑫; 王琰; 窦小楠; 孟洁; 岳建伟; 雷添杰

doi:10.13928/j.cnki.wrahe.2026.02.017

水利水电技术（中英文） ›› 2026, Vol. 57 ›› Issue (2) : 224 -240. DOI: 10.13928/j.cnki.wrahe.2026.02.017

运行管理

自然资源时空数据管理技术研究进展

金子鑫 ¹ ,
王琰 ²^,³ ,
窦小楠 ¹ ,
孟洁 ¹ ,
岳建伟 ²^,³ ,
雷添杰 ⁴

作者信息 +

Research progress on spatiotemporal data management technologies for natural resources

Zixin JIN ¹ ,
Yan WANG ²^,³ ,
Xiaonan DOU ¹ ,
Jie MENG ¹ ,
Jianwei YUE ²^,³ ,
Tianjie LEI ⁴

Author information +

文章历史 +

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摘要

【目的】自然资源时空数据管理技术一直是自然资源领域的研究热点。为了理清自然资源时空数据管理技术的研究现状和发展趋势，【方法】使用结构关系法，围绕自然资源数据库构建分析框架，以各分体结构技术为切入点，包括自然资源数据建模方法、时空索引技术和数据更新方法，详细梳理了自然资源时空数据管理关键技术与方法的内容与关系，根据其整体与局部的关联，对自然资源时空数据管理技术在数据应用、数据建模、数据检索和数据更新等未来发展趋势进行了分析。【结果】目前的自然资源时空数据管理技术在静态的实体表达、结构化的时空索引、同类型的数据更新和二维数据的存储共享的研究已较为成熟，能够满足单一领域或某一模态下的数据数字化管理，但在多源数据融合、多模态模型建模和数据动态索引更新仍存在技术瓶颈，【结论】面向自动化、智能化的管理目标构建多模态、动态、精细化的数据管理技术将是下一阶段的研究重点。

Abstract

[Objective] Spatiotemporal data management technologies for natural resources have long been a research focus in the field of natural resources. The aim is to clarify the current research status and identify future development trends in this area. [Methods] A structural relational approach is employed to construct an analytical framework centered on natural resource databases. Taking various component technologies as entry points—including natural resource data modeling method, spatiotemporal indexing techniques, and data update method —the content and relationships of key technologies and method for spatiotemporal data management of natural resources are systematically reviewed. Based on the connections between the whole and its parts, future development trends of spatiotemporal data management technologies for natural resources in data application, data modeling, data retrieval, and data updating are analyzed. [Results] At present, technologies for spatiotemporal data management in natural resources are relatively mature in areas such as static entity representation, structured spatiotemporal indexing, homogeneous data updating, and 2 D data storage and sharing. These technologies can effectively support digital data management within a single domain or under a specific modality. However, there are still technical bottlenecks in multi-source data fusion, multimodal model construction, and dynamic data indexing and updating. [Conclusion] To achieve the goals of automated and intelligent data management, future research should focus on the development of multimodal, dynamic, and refined data management technologies.